Acronis International Gmbh

Philipp Gysel

Stanislav Protasov

Serg Bell

Candid Wuest

Dinil Mon Divakaran

Irina Lukasheva

Kenneth Nwafor

Systems and methods implement artificial intelligence to automatically generate malware detection rules. In a first phase, an AI model is trained on large amounts of data, so the AI model can learn to distinguish between benign applications and different malware families. In a second phase, the AI model is queried with new malware samples, and systems and methods propose new malware detection rules for those samples.

AI assisted generation of rules for malware detection with transformer attention mechanism

Systems and methods for verifying a production system automatically by testing a mirror copy of the production system on a testing computer. The system includes a mirror update transporter to deliver a mirror update from the production system to the mirror system, a mounting module to apply the mirror update to the mirror system, a testing computer on which the mirror system is running, a testing module to automatically execute a set of tests on the mirror system, and a communication module to communicate the results of the tests.

System and method for evaluating active backups using penetration testing

Systems and methods for the protection of information systems utilize business impact analysis (BIA) data to assess the risk of security mitigation operations. A detected security incident is enriched using BIA data. A proposed mitigation action and a risk of implementing the proposed mitigation action are determined using the enriched data so that an administrator user can understand the impact or risk to the business for the proposed mitigation action.

Systems and methods of using business impact analysis data to assess the risk of security mitigation steps

Disclosed herein are systems and method for detecting vulnerabilities in devices within a network. In one exemplary aspect, a method includes: intercepting a plurality of packets in a network with a plurality of devices; identifying a subset of packets corresponding to a first device of the network; extracting and embedding a plurality of features representing device behavior from the subset of packets into an input vector; executing a machine learning algorithm that receives the input vector and outputs a device type of the first device, wherein the machine learning algorithm is trained using a training dataset including network traffic information labelled with device types and a list of attributes of each respective device type; performing, for the first device, a security scan specific to the device type; and in response to detecting a vulnerability based on the security scan, generating a vulnerability assessment report for the first device.

Systems and methods for detecting vulnerabilities in internet-of-things (IOT) devices

Systems and methods for verifying a production system automatically by testing a backup copy of the production system. The system comprises a backup transporter configured to copy or move a set of backup files from the backup generated of the production system and replicate the set of files on a testing computer, a mounting module configured to instantiate a copy of the production system on the testing computer, and a testing module configured to apply a set of automatic pre-defined tests to the application on the testing computer to analyze the application for vulnerabilities and defects, and generate a list of results of execution of each of the pre-defined tests on the testing computer, the list of results indicative of the vulnerabilities and defects.

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Bern, Switzerland

Philipp Gysel